| With the rapidly development and maturity of ubiquitous computing technologies (e.g. Radio Frequency IDentification-RFID, Personal Digital Assistant-PDA, and ZigBee), a novel and next generation advanced manufacturing paradigm,——Ubiquitous Computing-based Manufacturing (U-Manufacturing) is coming into being. Applying U-Manufacturing on discrete production shop floor could solve these problems such as data gap between top plan and bottom execution, unawareness of exceptional situation or event, information acquisition in a delayed manner, and cover the shortage of traditional manufacturing execution system, thus achieve lean, real-time, online and remote management of business process on shop floor. Under the supports from the National Natural Science Foudation of China (No.50675201) and Industrial Project of Major S&T Special Fund (Priority Subject) of Zhejiang province (No.2008C01060-1), several key issues to deploy or implement U-Manufacturing on discrete shop floor, including construction of U-Manufacturing operational environment, transformation of ubiquitous data (typicallly logistics data and quality data) into information and personalized information service for different users just in time were studied separately.To solve the problem of constructing U-Manufacturing operational environment, a quantitative and qualitative-combined method to determine the Data Capture Nodes (DCNs) and the Tagged Objects (TOs) was established. First, the capability maturity model of U-Manufacturing and influence factors of application capability domain were built, then Analytic Hierarchy Process (AHP) was used to determine the factors'weight whereas Fuzzy Comprehensive Evaluation (FCE) was applied to estimate the capability maturity level, thus obtain the application area of U-Manufacturing within enterprise. Furthermore, a bottleneck-based method was proposed to find out the suitable DCNs in the application area. Next, Fuzzy Delphi Method (FDM) was used to identify the key factors which decide if one material should be tagged or not. On that basis, Fuzzy C-Means (FCM) method was introduced to divide the same type of materials into two clusters, thus determine the important objects that need to be tagged.To solve the problem of transforming ubiquitous logistics data into information, a semantic analysis method based on Complex Event Process (CEP) technique was proposed. First, RFID tags were labeled on materials and readers were fixed at the workstation, the tags being read by the readers was treated as an occurrence of simple event, which could be also called RFID Reading Event (RRE). Then, five event operators of CEP were used to aggregate RRE into six Logistics Complex Events (LCEs), which were material outbound event, material workstation-arrival event, material reworking event, material scrapping event, material final-inspection event and material warehouse-entry event, and each of them was defined formally and its aggregation process was described. Next, based on the relationship between six LCEs and working processes, a Logistics Status Matrix (LSM) was constructed to store all of the LCEs for a single production lot execution. Finally, the logistics progress of single production lot was justified in detail with the parameters (including rank and element) of LSM in a real-time manner.To solve the problem of transforming ubiquitous quality data into quality information, a semantic analysis method based on quality index was proposed. First, the result of quality data acquisition using mobile RFID reader was treated as a quality data particle, its formal description was presented and its attributes'sources were analyzed. Next, six Key Quality Indexes (KQIs) including quality qualification rate, quality loss rate, quality rework rate, quality first-pass-yield, quality stability rate and quality affection rate were given out and defined, where the fisrt four indexes were existed indexes and the last two indexes were new indexes. Based on the data quality particle and its attributes, the computing process of each quality index was given out under the normal and abnormal patterns seperately. Finally, combined together with six indexes and working processes, a Quality Status Matrix (QSM) was constructed to store all of the KQIs for a single production lot execution, and the parameters (mainly element) of QSM were also analyzed to reflect the material's quality timely.To solve the problem of providing information to different users just on demand, an information service method based on Business Process Context (BPC) on shop floor was proposed. First, a BPC dynamic modeling technique based on material and task was proposed, its basic modeling elements and three modeling steps (including initialization of BPC template, evolution of BPC template and embedding of LSM and QSM) were also presented. Next, from the user's perspective, the elements of U-Manufacturing Information Service Rule (ISR) were given out and its description or setting processes (including drafting, verification and saving) were discussed. The relational database table was suggested to store ISR. Finally, the generation processes of typical service information were analyzed based on BPC and ISR.Basic application procedures of the above methods were given out in order to guide and assist the discrete manufacturing enterprises to use them on their production shop floor smoothly. Then, the feasibility of the proposed methods was proved with four specific cases in a small and medium-sized automobile motor manufacturer.In the end, all of the above researches were summarized and there main innovative points were given out, several possible research directions of U-Manufacturing which should be pay special attention to were also discussed.
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